Average treatment effects standardized to treated covariates

att() computes the average treatment effect on the treated.

Usage

att(
  fit,
  newdata = NULL,
  y = NULL,
  type = c("mean", "rmean"),
  cutoff = NULL,
  interval = "credible",
  level = 0.95,
  nsim_mean = 200L,
  show_progress = TRUE
)

Arguments

fit

A "causalmixgpd_causal_fit" object from run_mcmc_causal().

newdata

Ignored for marginal estimands. If supplied, a warning is issued and training data are used.

y

Ignored for marginal estimands. If supplied, a warning is issued and training data are used.

type

Character; type of mean treatment effect:

• "mean" (default): ordinary mean ATE

• "rmean": restricted-mean ATE (requires cutoff)

cutoff

Finite numeric cutoff for restricted mean; required for type = "rmean", ignored otherwise.

interval

Character or NULL; type of credible interval:

• NULL: no interval

• "credible" (default): equal-tailed quantile intervals

• "hpd": highest posterior density intervals

level

Numeric credible level for intervals (default 0.95 for 95 percent CI).

nsim_mean

Number of posterior predictive draws used by simulation-based mean targets. Ignored for analytical ordinary means.

show_progress

Logical; if TRUE, print step messages and render progress where supported.

Value

An object of class "causalmixgpd_ate" containing the ATT summary, optional intervals, and the arm-specific predictive objects used in the aggregation. The returned object includes a top-level $fit_df data frame for direct extraction.

Details

The estimand is $$\mathrm{ATT} = E\{Y(1) - Y(0) \mid A = 1\},$$ approximated by marginalizing over the empirical covariate distribution of treated units.

See also

ate, qtt, cate.

Examples

if (FALSE) { # \dontrun{
cb <- build_causal_bundle(y = y, X = X, A = A, backend = "sb", kernel = "normal", components = 6)
fit <- run_mcmc_causal(cb, show_progress = FALSE)
att(fit, interval = "credible", nsim_mean = 100)
} # }